Connect systems
CRM systems, calendars, websites, databases, internal APIs and external services can be exposed as clearly bounded tools. The agent receives only the functions and data access needed for its task.
AI AGENTS & ASSISTANTS
AI agents that can act within clear boundaries.
I build AI agents and digital assistants that can work with defined tools, data sources and APIs. The focus is not unrestricted autonomy, but useful actions inside a controlled process with explicit permissions, approvals and traceability.
- Connect selected tools and APIs
- Model roles, approvals and permissions
- Keep actions traceable and deliberately limited
WHAT AN AI AGENT CAN DO
From assistant to an action-capable system.
A useful AI agent should not receive blanket access to every system. I define which information it may read, which functions it can call and where human approval, deterministic rules or additional checks remain necessary.
Orchestrate workflows
Multi-step tasks can combine conditions, questions, tool calls, escalations and approval points so more complex processes remain understandable and controllable.
Limit permissions
Read and write access, roles, logs and technical guardrails are designed into the system so it remains clear what the agent may do and when a human decision is required.
TYPICAL APPLICATIONS
Agents for specific business tasks.
The strongest use cases appear when an AI agent does more than answer questions and instead connects defined work steps with existing company systems.
Internal assistants
- Search documents and internal knowledge
- Combine information from multiple systems
- Prepare drafts, analyses and recommended actions
- Guide employees through defined processes
Action-taking agents
- Update records according to rules
- Prepare appointments or customer requests
- Create tasks in connected tools
- Execute changes only after defined approval
TECHNICAL IMPLEMENTATION
An agent needs more than a language model.
A robust agent combines the model with tools, APIs, data sources, state logic, permissions, logging and error handling. These components are designed as one system so behaviour, cost and allowed actions remain observable.
Autonomy is an architecture decision
Not every task should run fully autonomously. Depending on the risk, an AI agent may only recommend actions, prepare them for approval or act independently within narrow permissions. Autonomy is deliberately graduated rather than switched on globally.
Want to integrate an AI agent into a real business process?
Describe the task, existing systems, relevant data sources and desired actions. I will assess how an agent can be integrated and which roles, approvals and technical boundaries make sense for reliable operation.